Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Estimating Structured Vector Autoregressive Models
Authors: Igor Melnyk, Arindam Banerjee
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real data with a variety of structures are presented, validating theoretical results. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, University of Minnesota, Twin Cities |
| Pseudocode | No | The paper does not include a pseudocode block or clearly labeled algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We used the NASA flight dataset from (nas), consisting of over 100,000 flights, each having a record of about 250 parameters, sampled at 1 Hz. (nas) NASA Aviation Safety Dataset. Available at https://c3.nasa.gov/dashlink/projects/85/. |
| Dataset Splits | Yes | For each flight we separately fitted a first-order VAR model using five approaches and performed 5-fold cross validation to select λ, achieving smallest prediction error. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., specific libraries, frameworks, or solvers with their versions). |
| Experiment Setup | Yes | To evaluate the estimation problem with L1 norm, we simulated a first-order VAR process for different values of p [10, 600], s [4, 260], and N [10, 5000]. Regularization parameter was varied in the range λN (0, λmax)... For Sparse Group we set α = 0.5, while for OWL the weights c1, . . . , cp were set as a monotonically decreasing sequence. |